Investment Decision Model of Rural Power Grid Projects Considering Contribution of Rural Revitalization

doi:10.11930/j.issn.1004-9649.202306125

Abstract

Abstract:

Deepening the rural revitalization strategy puts forward higher requirements for rural grid development, quantifying the contribution of rural grid projects in the new era and assisting rural grids to achieve accurate investment become key. Based on the new requirements of the rural revitalization strategy for the development of modern rural power grids, a rural power grid contribution evaluation index system comprising four dimensions, including safety and relia-bility, precise service, green and low-carbon, and digital intelligence, is constructed, and the minimum cross-entropy model is used to combine the FUCOM-variance coefficient method to assign weights to the indexes and quantify the contribution of rural power grids by applying the weighted Marxian distance TOPSIS. With the objective of optimising the contribution of rural grid units and maximising financial benefits, a rural grid investment decision model was con-structed and solved based on the c-DPEA algorithm. The simulation results of a batch of 20 rural grid projects in a county show that the proposed quantitative model can scientifically evaluate the contribution of rural grid projects, and the investment decision model can provide an effective reference for precise investment in rural grid.

Key words: rural power grid, contribution to rural revitalization, investment decision, portfolio empowerment, multi-objective optimization

Huiru ZHAO, Manyu YAO, Bingkang LI, Guanglong XIE, Zhihua DING, Zhenda HU. Investment Decision Model of Rural Power Grid Projects Considering Contribution of Rural Revitalization[J]. Electric Power, 2024, 57(6): 181-192.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202306125

https://www.electricpower.com.cn/EN/Y2024/V57/I6/181

Figures/Tables 12

References 26

1	毛世平, 张琳, 何龙娟, 等. 我国农业农村投资状况及未来投资重点领域分析[J]. 农业经济问题, 2021, 42 (7): 47- 56.
	MAO Shiping, ZHANG Lin, HE Longjuan, et al. Analysis on agricultural and rural investment status and key areas for future investment in China[J]. Issues in Agricultural Economy, 2021, 42 (7): 47- 56.
2	王利利, 贾梦雨, 韩松, 等. 基于TOPSIS—灰色关联度的农网投资效益与风险能力综合评价[J]. 电力科学与技术学报, 2020, 35 (4): 76- 83.
	WANG Lili, JIA Mengyu, HAN Song, et al. Synthesized evaluation of investment efficiency and risk ability of rural network based on TOPSIS-gray incidence[J]. Journal of Electric Power Science and Technology, 2020, 35 (4): 76- 83.
3	赵大光. 以“整村改造” 为核心的农配网规划与实施[J]. 企业管理, 2016, (增刊2): 324- 325.
4	ILBAHAR E, CEBI S, KAHRAMAN C. A state-of-the-art review on multi-attribute renewable energy decision making[J]. Energy Strategy Reviews, 2019, 25, 18- 33. DOI
5	王泽祺, 刘友波, 任鹏哲, 等. 配电网精准投资理论与关键技术综述[J]. 电网技术, 2022, 46 (7): 2665- 2678.
	WANG Zeqi, LIU Youbo, REN Pengzhe, et al. Review of key technologies and theories of cost-effective investment orienting to distribution networks[J]. Power System Technology, 2022, 46 (7): 2665- 2678.
6	杨红磊, 盛万兴, 王金宇, 等. 基于模糊评价方法的农网改造升级工程投资效果分析[J]. 电工电能新技术, 2015, 34 (2): 55- 60.
	YANG Honglei, SHENG Wanxing, WANG Jinyu, et al. Fuzzy evaluation method of investment efficiency evaluation in rural distribution network transform and upgrade project[J]. Advanced Technology of Electrical Engineering and Energy, 2015, 34 (2): 55- 60.
7	檀晓林, 胡诗尧, 张菁, 等. 基于主成分分析与加权TOPSIS的县域配电网投资效益评估方法[J]. 电力科学与技术学报, 2022, 37 (3): 133- 139.
	TAN Xiaolin, HU Shiyao, ZHANG Jing, et al. Investment benefit assessment based on PCA and weighted TOPSIS in county distribution network[J]. Journal of Electric Power Science and Technology, 2022, 37 (3): 133- 139.
8	李彦吉, 王鑫陶, 康赫然, 等. 考虑效率、效益与碳减排提升的含可再生能源配电网投资优选模型[J]. 可再生能源, 2021, 39 (12): 1662- 1668.
	LI Yanji, WANG Xintao, KANG Heran, et al. An investment optimization model for distribution network containing renewable energy considering efficiency benefits and carbon emission reduction[J]. Renewable Energy Resources, 2021, 39 (12): 1662- 1668.
9	宋忧乐, 聂鼎, 王科, 等. 配电网规划中基于供给侧可靠性指标及其时空分解的精准投资问题研究[J]. 电测与仪表, 2023, 60 (5): 126- 131.
	SONG Youle, NIE Ding, WANG Ke, et al. Research on precision investment problems based on supply-side reliability index and its spatio-temporal decomposition in distribution network planning[J]. Electrical Measurement & Instrumentation, 2023, 60 (5): 126- 131.
10	潘笑, 邵建伟, 刘士齐, 等. 基于网格化规划的城市配电网精准投资策略[J]. 智慧电力, 2022, 50 (2): 1- 8.
	PAN Xiao, SHAO Jianwei, LIU Shiqi, et al. Accurate investment strategy for urban distribution network based on grid planning[J]. Smart Power, 2022, 50 (2): 1- 8.
11	马杰, 李秋燕, 王利利, 等. 考虑GIS信息的城市配电系统网格化动态投资策略优化[J]. 电力建设, 2021, 42 (11): 54- 62.
	MA Jie, LI Qiuyan, WANG Lili, et al. Optimization of grid dynamic investment strategy for urban distribution system considering GIS information[J]. Electric Power Construction, 2021, 42 (11): 54- 62.
12	贾梦雨, 李锰, 韩松, 等. 基于博弈论组合赋权的农村电网综合评价体系研究[J]. 电力科学与技术学报, 2020, 35 (2): 69- 75.
	JIA Mengyu, LI Meng, HAN Song, et al. Research on rural power system comprehensive evaluation system based on game theory combination weights[J]. Journal of Electric Power Science and Technology, 2020, 35 (2): 69- 75.
13	郑建新, 王艳君, 张松. 基于智能软开关的农村配电网无功优化[J]. 河北农业大学学报, 2022, 45 (4): 123- 129.
	ZHENG Jianxin, WANG Yanjun, ZHANG Song. Reactive power optimization of rural distribution network based on intelligent soft switch[J]. Journal of Hebei Agricultural University, 2022, 45 (4): 123- 129.
14	杨莘博, 谭忠富, 林宏宇, 等. 计及需求响应及储能聚合商的分布式农村清洁能源系统运行优化模型[J]. 系统工程理论与实践, 2022, 42 (12): 3319- 3334.
	YANG Shenbo, TAN Zhongfu, LIN Hongyu, et al. Operation optimization model of distributed rural clean energy system considering demand response and energy storage aggregator[J]. Systems Engineering-Theory & Practice, 2022, 42 (12): 3319- 3334.
15	刘芳, 门艳, 陈丽, 等. 农网智能化项目实施及技术经济评价研究[J]. 企业管理, 2016, (增刊2): 230- 231.
16	PAMUČAR D, STEVIĆ Ž, SREMAC S. A new model for determining weight coefficients of criteria in MCDM models: full consistency method (FUCOM)[J]. Symmetry, 2018, 10 (9): 393. DOI
17	陈道君, 钟伟, 杨楠, 等. 基于综合赋权的电磁环网开环方案评价方法研究[J]. 高压电器, 2023, 59 (1): 77- 85.
	CHEN Daojun, ZHONG Wei, YANG Nan, et al. Research on evaluation method of open-loop scheme of electromagnetic loop network based on comprehensive weight[J]. High Voltage Apparatus, 2023, 59 (1): 77- 85.
18	徐其春, 张乃夫, 葛磊蛟, 等. 基于二项系数和变异系数混合的电网投资决策评价计算方法[J]. 中国测试, 2021, 47 (12): 6- 13.
	XU Qichun, ZHANG Naifu, GE Leijiao, et al. Calculation model of power grid investment decision evaluation based on the mixture of binomial coefficient and variant coefficient[J]. China Measurement & Test, 2021, 47 (12): 6- 13.
19	何铭, 邹艳丽, 梁明月, 等. 基于多属性决策的电力网络关键节点识别[J]. 复杂系统与复杂性科学, 2020, 17 (3): 27- 37.
	HE Ming, ZOU Yanli, LIANG Mingyue, et al. Critical node identification of a power grid based on multi-attribute decision[J]. Complex Systems and Complexity Science, 2020, 17 (3): 27- 37.
20	柯方超, 陈然, 唐学军, 等. 基于信息熵的电网基建参与单位绩效评价方法[J]. 武汉理工大学学报(信息与管理工程版), 2022, 44 (3): 402- 409.
	KE Fangchao, CHEN Ran, TANG Xuejun, et al. Performance appraisal of participates in the projects for the construction of power system infrastructure[J]. Journal of Wuhan University of Technology (Information & Management Engineering), 2022, 44 (3): 402- 409.
21	杜岩, 谢从珍, 李彦丞, 等. 基于加权马氏距离型TOPSIS算法的10 kV配电网雷害风险评估[J]. 中国电力, 2022, 55 (4): 108- 116.
	DU Yan, XIE Congzhen, LI Yancheng, et al. Lightning risk assessment of 10 kV distribution line based on TOPSIS algorithm improved by weighted mahalanobis distance[J]. Electric Power, 2022, 55 (4): 108- 116.
22	MING M J, TRIVEDI A, WANG R, et al. A dual-population-based evolutionary algorithm for constrained multiobjective optimization[J]. IEEE Transactions on Evolutionary Computation, 2021, 25 (4): 739- 753.
23	XU B, ZHANG G Y, LI K, et al. Reactive power optimization of a distribution network with high-penetration of wind and solar renewable energy and electric vehicles[J]. Protection and Control of Modern Power Systems, 2022: 1–13.
24	DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6 (2): 182- 197. DOI
25	ZHANG Q F, LI H. MOEA/D: a multiobjective evolutionary algorithm based on decomposition[J]. IEEE Transactions on Evolutionary Computation, 2007, 11 (6): 712- 731. DOI
26	ZHAO H R, LI B K, LU H, et al. Economy-environment-energy performance evaluation of CCHP microgrid system: a hybrid multi-criteria decision-making method[J]. Energy, 2022, 240, 122830. DOI

指标	$ {w}_{i} $	指标	$ {w}_{i} $	指标	$ {w}_{i} $
$ {T}_{11} $	0.138	$ {T}_{22} $	0.070	$ {T}_{41} $	0.067
$ {T}_{12} $	0.112	$ {T}_{23} $	0.112	$ {T}_{42} $	0.047
$ {T}_{13} $	0.078	$ {T}_{31} $	0.121	$ {T}_{43} $	0.360
$ {T}_{21} $	0.106	$ {T}_{32} $	0.113

指标	$ {w}_{i} $	指标	$ {w}_{i} $	指标	$ {w}_{i} $
$ {T}_{11} $	0.138	$ {T}_{22} $	0.070	$ {T}_{41} $	0.067
$ {T}_{12} $	0.112	$ {T}_{23} $	0.112	$ {T}_{42} $	0.047
$ {T}_{13} $	0.078	$ {T}_{31} $	0.121	$ {T}_{43} $	0.360
$ {T}_{21} $	0.106	$ {T}_{32} $	0.113

指标	$ y''_i $	$ {S}_{i} $	$ {V}_{i} $	$ {w}_{i} $
$ {T}_{11} $	0.2232	0.0143	0.0640	0.0807
$ {T}_{12} $	0.2232	0.0146	0.0652	0.0822
$ {T}_{13} $	0.2227	0.0211	0.0948	0.1194
$ {T}_{21} $	0.2235	0.0061	0.0272	0.0343
$ {T}_{22} $	0.2231	0.0156	0.0697	0.0879
$ {T}_{23} $	0.2213	0.0331	0.1497	0.1887
$ {T}_{31} $	0.2211	0.0341	0.1543	0.1945
$ {T}_{32} $	0.2234	0.0109	0.0490	0.0617
$ {T}_{41} $	0.2234	0.0099	0.0444	0.0560
$ {T}_{42} $	0.2234	0.0090	0.0404	0.0509
$ {T}_{43} $	0.2235	0.0077	0.3470	0.4370

指标	$ y''_i $	$ {S}_{i} $	$ {V}_{i} $	$ {w}_{i} $
$ {T}_{11} $	0.2232	0.0143	0.0640	0.0807
$ {T}_{12} $	0.2232	0.0146	0.0652	0.0822
$ {T}_{13} $	0.2227	0.0211	0.0948	0.1194
$ {T}_{21} $	0.2235	0.0061	0.0272	0.0343
$ {T}_{22} $	0.2231	0.0156	0.0697	0.0879
$ {T}_{23} $	0.2213	0.0331	0.1497	0.1887
$ {T}_{31} $	0.2211	0.0341	0.1543	0.1945
$ {T}_{32} $	0.2234	0.0109	0.0490	0.0617
$ {T}_{41} $	0.2234	0.0099	0.0444	0.0560
$ {T}_{42} $	0.2234	0.0090	0.0404	0.0509
$ {T}_{43} $	0.2235	0.0077	0.3470	0.4370

指标类型	指标	w_i	排序	组合权重
安全可靠$ {T}_{1} $	农网供电可靠率预计提升程度$ {T}_{11} $	0.1089	3	0.3083
	户均配电容量预计提升程度$ {T}_{12} $	0.0991	5
	农网综合电压合格率预计提升程度$ T $	0.0996	4
精准服务$ {T}_{2} $	单位投资预计增供电量$ {T}_{21} $	0.0622	9	0.2972
	节能型配变比例预计提升程度$ {T}_{22} $	0.0808	7
	农村电气化率预计提升程度$ {T}_{23} $	0.1501	2
绿色低碳$ {T}_{3} $	可再生能源渗透率预计提升程度$ {T}_{31} $	0.1584	1	0.2424
绿色低碳$ {T}_{3} $	单位投资预计新增碳排放量$ {T}_{32} $	0.0862	6	0.2424
数字智能$ {T}_{4} $	智能终端覆盖率预计提升程度$ {T}_{41} $	0.0632	8	0.1521
	电力光纤长度比例预计提升程度$ {T}_{42} $	0.0505	10
	智能变电站比率预计提升程度$ {T}_{43} $	0.0409	11